{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import pickle as pkl\n",
    "import time\n",
    "from random import shuffle\n",
    "import pandas as pd \n",
    "import spectral\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab as pl\n",
    "import scipy\n",
    "import IndianPinesMLP\n",
    "from collections import Counter\n",
    "import Spatial_dataset as input_data\n",
    "import patch_size\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import scipy.io as io\n",
    "DATA_PATH = os.path.join(os.getcwd(),\"Data\")\n",
    "input_image = scipy.io.loadmat(os.path.join(DATA_PATH, 'Indian_pines.mat'))['indian_pines']\n",
    "output_image = scipy.io.loadmat(os.path.join(DATA_PATH, 'Indian_pines_gt.mat'))['indian_pines_gt']\n",
    "\n",
    "model_name = 'model-MLP-1X1.ckpt-49999'\n",
    "input_image = np.rot90(input_image)\n",
    "output_image = np.rot90(output_image)\n",
    "height = output_image.shape[0]\n",
    "width = output_image.shape[1]\n",
    "PATCH_SIZE = patch_size.patch_size\n",
    "\n",
    "\n",
    "fc1 = 500\n",
    "fc2 = 350\n",
    "fc3 = 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## Scaling Down the image to 0 - 1\n",
    "\n",
    "input_image = input_image.astype(float)\n",
    "input_image -= np.min(input_image)\n",
    "input_image /= np.max(input_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def mean_array(data):\n",
    "    mean_arr = []\n",
    "    for i in range(data.shape[0]):\n",
    "        mean_arr.append(np.mean(data[i,:,:]))\n",
    "    return np.array(mean_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def Patch(data,height_index,width_index):\n",
    "    transpose_array = data.transpose((2,0,1))\n",
    "    #print transpose_array.shape\n",
    "    height_slice = slice(height_index, height_index+PATCH_SIZE)\n",
    "    width_slice = slice(width_index, width_index+PATCH_SIZE)\n",
    "    patch = transpose_array[:, height_slice, width_slice]\n",
    "    #print patch.shape\n",
    "    mean = mean_array(transpose_array)\n",
    "    mean_patch = []\n",
    "    for i in range(patch.shape[0]):\n",
    "        mean_patch.append(patch[i] - mean[i])\n",
    "    mean_patch = np.asarray(mean_patch)\n",
    "    patch = mean_patch.transpose((1,2,0))\n",
    "    patch = patch.reshape(-1,patch.shape[0]*patch.shape[1]*patch.shape[2])\n",
    "    #print patch.shape\n",
    "    return patch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def placeholder_inputs(batch_size):\n",
    "    \"\"\"Generate placeholder variables to represent the input tensors.\n",
    "    These placeholders are used as inputs by the rest of the model building\n",
    "    code and will be fed from the downloaded data in the .run() loop, below.\n",
    "    Args:\n",
    "    batch_size: The batch size will be baked into both placeholders.\n",
    "    Returns:\n",
    "    images_placeholder: Images placeholder.\n",
    "    labels_placeholder: Labels placeholder.\n",
    "    \"\"\"\n",
    "    # Note that the shapes of the placeholders match the shapes of the full\n",
    "    # image and label tensors, except the first dimension is now batch_size\n",
    "    # rather than the full size of the train or test data sets.\n",
    "    images_placeholder = tf.placeholder(tf.float32, shape=(batch_size, IndianPinesMLP\n",
    "                                                           .IMAGE_PIXELS))\n",
    "    labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size))\n",
    "    return images_placeholder, labels_placeholder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fill_feed_dict(data_set, images_pl, labels_pl):\n",
    "    \"\"\"Fills the feed_dict for training the given step.\n",
    "    A feed_dict takes the form of:\n",
    "    feed_dict = {\n",
    "      <placeholder>: <tensor of values to be passed for placeholder>,\n",
    "      ....\n",
    "    }\n",
    "    Args:\n",
    "    data_set: The set of images and labels, from input_data.read_data_sets()\n",
    "    images_pl: The images placeholder, from placeholder_inputs().\n",
    "    labels_pl: The labels placeholder, from placeholder_inputs().\n",
    "    Returns:\n",
    "    feed_dict: The feed dictionary mapping from placeholders to values.\n",
    "    \"\"\"\n",
    "    # Create the feed_dict for the placeholders filled with the next\n",
    "    # `batch size ` examples.\n",
    "    images_feed, labels_feed = data_set.next_batch(batch_size)\n",
    "    feed_dict = {\n",
    "      images_pl: images_feed,\n",
    "      labels_pl: labels_feed,\n",
    "    }\n",
    "    return feed_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def do_eval(sess,\n",
    "            eval_correct,\n",
    "            images_placeholder,\n",
    "            labels_placeholder,\n",
    "            data_set):\n",
    "    \"\"\"Runs one evaluation against the full epoch of data.\n",
    "    Args:\n",
    "    sess: The session in which the model has been trained.\n",
    "    eval_correct: The Tensor that returns the number of correct predictions.\n",
    "    images_placeholder: The images placeholder.\n",
    "    labels_placeholder: The labels placeholder.\n",
    "    data_set: The set of images and labels to evaluate, from\n",
    "      input_data.read_data_sets().\n",
    "    \"\"\"\n",
    "    # And run one epoch of eval.\n",
    "    true_count = 0  # Counts the number of correct predictions.\n",
    "    steps_per_epoch = data_set.num_examples // batch_size\n",
    "    num_examples = steps_per_epoch * batch_size\n",
    "    for step in xrange(steps_per_epoch):\n",
    "        feed_dict = fill_feed_dict(data_set,\n",
    "                                   images_placeholder,\n",
    "                                   labels_placeholder)\n",
    "        true_count += sess.run(eval_correct, feed_dict=feed_dict)\n",
    "    precision = float(true_count) / num_examples\n",
    "    print('  Num examples: %d  Num correct: %d  Precision @ 1: %0.04f' %\n",
    "        (num_examples, true_count, precision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def decoder():\n",
    "    \n",
    "    #data_sets = input_data.read_data_sets('Test1.mat','test')\n",
    "    \n",
    "    with tf.Graph().as_default():\n",
    "        \n",
    "        images_placeholder, labels_placeholder = placeholder_inputs(1)\n",
    "\n",
    "        logits = IndianPinesMLP.inference(images_placeholder,\n",
    "                                 fc1,\n",
    "                                 fc2,\n",
    "                                 fc3)\n",
    "        \n",
    "        eval_correct = IndianPinesMLP.evaluation(logits, labels_placeholder)\n",
    "        \n",
    "        sm = tf.nn.softmax(logits)\n",
    "        \n",
    "        saver = tf.train.Saver()\n",
    "\n",
    "        sess = tf.Session()\n",
    "        \n",
    "        saver.restore(sess,model_name)\n",
    "        \n",
    "        temp = []\n",
    "\n",
    "        outputs = np.zeros((height,width))\n",
    "        predicted_results = [[0 for i in range(width)]for x in range(height)]\n",
    "        for i in range(height-PATCH_SIZE+1):\n",
    "            for j in range(width-PATCH_SIZE+1):\n",
    "                target = int(output_image[i+PATCH_SIZE/2, j+PATCH_SIZE/2])\n",
    "                if target == 0 :\n",
    "                    continue\n",
    "                else :\n",
    "                    image_patch=Patch(input_image,i,j)\n",
    "                    #print image_patch\n",
    "                    prediction = sess.run(sm, feed_dict = {images_placeholder:image_patch})\n",
    "                    #print prediction\n",
    "                    temp1 = np.argmax(prediction)+1\n",
    "                    #print temp1\n",
    "                    outputs[i+PATCH_SIZE/2][j+PATCH_SIZE/2] = temp1\n",
    "                    predicted_results[i+PATCH_SIZE/2][j+PATCH_SIZE/2] = prediction\n",
    "                    \n",
    "    return outputs,predicted_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Reshape:0\", shape=(1, 220), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "predicted_image,predicted_results = decoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Zu0NhDB+9jJIZfNsz8ZBFGq5510pPTb2Msp8G3qDRbEgHcoEiUuCvVsSS/PnE0Tm6+Tkn\nrrTn3UAld7D8ShIU81gJfAi4g8h6boUIkk9bHDUxQBOTU8oy8HAry2yMyHm8+BlgnA6GGcM3+UAl\n8EHMFA0hoiBJTzhKL6O8wkUOcJl6Yj+QXKQeSXrCUYbwcoxWDpAoKzbFWG8vtLeDz7fwc2dqbIR+\n6QSaSZKeEE529Cg8/TR0dob/2qEhOHXK+pgSXNImPb9/nNHRXgYHW/H5RuN67Cw8lJKz4ADkcnKk\n5zbIi58OhrlADwMkwurrMdbdDU1N8Oqr8NJLZmaFsETSJr3R0T4uX36Lzs7TDA9H8C0ZhcXkcT/X\nsJKieZ9XQS7pEVWZSz6DjLOHi7xGPY0yLg9OnoQf/xjefhv6pG3TSkmb9Hy+Ufr6GujtvRT3Y+eT\nwUbKuFbGVoRsDD/n6OYYKX5G09UF9fWwdy/s2gWXpW3Takmb9IRISLW18MQT8OabJgEKy0VV20kp\nVaCUel4pdVopdVIpdbNSqkgptUspVauU+oNSqsCqYEXy8eLnIj0cpZVOhu0Ox37d3XDoEJw+DSOy\niEssRFvQ7h+Bf9Nar8cU+zkDPAbs1lqvxVQvezzKY4gkNoqPP1LP0xynDjmzEbEX8eWtUiofuF1r\n/QiA1toH9CmlHsRMFgJ4CtiHSYQpo49RjtGKG8VyCskjw+6QHMtHgGYGqKPb7lDs1dFhzu4OHIjb\n2LqxgTG667oZ7Q1/dINyKXIrcskpzYlBZLEVTZveCqBTKfUk5izvEPA3QLnWug1Aa92qlCqLPszE\n0sIgL1JLC4PsYKMkPbGw8+fh+9+H/fvNYOQ4mFi4ypUW/gWfO93NiveuSLmk5wGuA/6j1vqQUurb\nmDO6mcXP5iyGdvbsb678XFy8huLiNVGEY3i9IwwMNMdtEaDZjOKjmQFaGZw+f1Rc4SPAebo5Tee0\n+chJb2AAXn99coZFSQls3mwGEp8/Dw0NcQvFP+ZneCyydlSXx0XH6Q6UK7xxphkFGeRX5ZOWbV8R\n3WiS3mWgUWt9KHj/BUzSa1NKlWut25RSFcCcX1tr1nwgisPPbnx8kObmt2lrO47XKw3jTuXFz+s0\n8hK19BH5usAJp6MDfvEL2LnT3L/uOvjiF+2NKQIBX4D2mnZ6zodXo7J4TTEZ2zMSM+kFk1qjUmqN\n1voscDdwMnh7BPgG8DDwohWBhh6Xn7GxAUZHe+N52GkWkcVairmRKgrItC0OJ9OY4gJX1r6Ih3RM\nlRarvwtrgMOYhZqUMols8+bQXpufb9rxmptNQkwg3iEv3qHwFvd0Z7hpOdxCZlHonwvt1wy2WPd3\nEu04vc8BTyul0oALwKcwC9k/p5R6FKgHdkR5jISzmFweZB3bqJC1MJwkC1Oe6m6L9/vPwDEmk949\n98Bf/3Vorz1+HP75n80lbwoUBxhqG+LSvksodxiXxRp8o9Y1E0WV9LTWx4AbZ3loezT7TXRpuCkk\nU87yZqHRnKSDE7RxLt49ti6gMHiz0tQlT5QyZ2+VlaG9dnTULAlZVTX/806ehIMHzfMTWMAXYHzQ\n3rnVMiNDxFUAzQEu8ww1ydnJo8NcxGjZMnj00YVLRz35pFnKMcGTnhMkTdLz+Ubp7j5HV1cdQ0P2\nzt9sZ4iXOc/JuftwFuTBxVYqWEeJhZE5wxg+BqWSiuHxQF7ews9717vMJfMf/2iGtURSX08ASZT0\nvN5hWlqO0NR00PalHlsY4EVqoyoblYWHv+S6pEx6IgI33gjbtkFOjrnMlaQXsaRJemB6brX22x0G\nGjMOLRpuFIEkW+/1CC0cpoXjqV5JJRIej7mlScdYtJIq6QlnO0wLP+Sw3WGIFCdJT9jC5YGqm6F8\ngeFsg63QdJDEmbOhpBK200nSEzHnI3DlNsGVBlU3waY/m/+1bcehrzGBkp5wPEl6IuaO0sobNKZG\nW164Q1ZE3CV80tNa4/eP4fUOEwg4o0fLjSITD54oyhVmkUZG4v/zAHCGTp7jZNSdOwLIyjJFCoaG\nzP3hYSk2GqaE/1T5/WO0tR2nvf0k/f3OWE9gMXm8h2qWTBuqHx4PLjZQamFUIim8+93wX/7L5JCV\nnTvhd7+zN6YEkwRJz0tXVx3NzW/bHcoVJWTzHqpTfmGgYbwMMn7VQOT0XMhaBGnZNgWWyLZsMbcJ\ng4Nm/m5/vylbJRaU8ElPOFcN7ezlIqfpxD9xaatMB0b1HWBB+URx991QWAgvvAC//73d0SQESXoi\nZurpZRfnGWKy/JBSULIOVv+JjYHFUryHrGzbBhs2mJXTzp832/x+6OxMiaotkZCkJ0Si83jg3nth\nxQpzv6cHnnkG9u2zNSynkqQnhJXsGLLidsPWreYGZo2N5mZobTW3XvsK6jpRtEtACiGcJj8fPvQh\nU4Y+1ArOKSRhz/S0DjAy0sPgYCvj487qtRpinIv0RDVOLxm0MHhV0QStYbAN2mtC20fPBfAOAQpT\nlt2JGpi+/FVDgykBb7eiIsiQlfhmStikFwj4aG+voaXlHYaGnLW2QAuD/JozKb/0YztDjDOj6o2G\nywegrz60fYwPQn8T5i/1+xDF0MfYaYArv2YgALt2QW2tnREZIyOTnRviioRNeloHGBpqp7v7nN2h\nXGWQcWrpsjsMxxpoMrewHVr4KY5w/rwkGwdL7esvIUTKsfVMr32Ohp3MzCJycspwu2NQMDGzCAqW\nRTYdIOCD/kZT78guOcAaiMlkDy9wFnO5ZqG8SvOWR1FIOuHoAPQ1wGBLcENmJqxZs/ACQLHU1wdn\nz5oxfCnM1qRXW7tz1u3l5VtZvvyO2CS9wmrY8GHIXxr+a71DcOoFe5NeKfDnxGa9uX7ge1ie9Mq3\nmrfclbCNKeHzj8GpX05JeoWF8JGPwP332xfUyZPw3e9K0rPz4HMVCEhLyyYzs5C0tKxZHlXk5S0m\nMzPMdfwyi6BopZkDVboR8haHH/BYP4R7XKtlACuAbTHYdw/EYkmOrEVmylksvsOcyjs8408lLQ2q\nq80MCrv4fKEtQpTkHPnd29fXyMhID0pd3eSolGLFiruorLwhvJ3mV8G6h6Biq/2JSwhhG0cmPZ9v\nBJ9vrhphis7OM4BiMJzLTE+mObvLKbMiRBGCgmXmDK9kLczy/SWELRyZ9Oan6eg4TU/PRbzeYbuD\nEfMo2wSbPw45pZL0hHMkYNJb6ExwhqxFULIeKm8w7XrRcKdD+RbTizub0V7oOAXDzhosbZe0bMgt\nN/XzhHCKhEx6YckphzUfMElv1o6RMLjTofp2qLpx9se7zprODkl6QjhW8ic9lwcy8swtWsplTl/m\nGuOXkZ9aXZRzKFxhlnZcfJ1Z9UwIJ4mqpUUp9bhS6qRS6rhS6mmlVLpSqkgptUspVauU+oNSqsCq\nYEViKFkHW/8Clt1mTo6FcJKIz/SUUtXAp4F1WutxpdSzmGGzG4DdWutvKqW+DDwOPGZJtMJ6fwTe\nCP48AhyOfpcuD3iyJOEJZ4rm8rYfGAdylFIBIAtowiS5O4LPeQrYhyQ953oF+J9T7suyrSLJRZz0\ntNY9SqlvYSYtDQO7tNa7lVLlWuu24HNalVL2DIzLKjadF5U3mMmfYro/AnuAvWDVcrRFqybf8mj7\njISIlWgub1cCXwCqgT7geaXUx7n6XMGec4fsYlh1Dyy71ZbDO95+4H9g6b/OolWwcYeZ/CKEU0XT\nkXED8LrWultr7Qd+BbwbaFNKlQMopSqA9ujDFEIIa0ST9GqBW5RSmUopBdwNnAJ2Ao8En/Mw8GJU\nEYrYsajUk3IHOy4yZOaFcL5o2vSOKaV+AryDKZZ9BPgBkAc8p5R6FKgHdlgRqIgBiy5ti1aY4SkV\n26wZDilELEU1OFlr/ffA38/Y3E1sqr2FZmIAcUaBDBSOk7wq03xatNLuSIRYWPLNyMgsguV3QNXN\nUFBtdzTOppAhKiLlJF/SS881YyaW37Hwc63i98L4AAx3gm8sfse1mTvDXM5mFaVWVWSR2ORP1Qoj\n3XDxFbj8FsxRDdqRojzLK1gKK+6GimtNMRshEoEkPSuMD0LLEWh6a/r2HKAMyLTwWCtxzNqv2SWw\n5BYoXW93JEKETpJeLK3BzEZebeE+czGzm60gbXoiBSVP0nN5ILvUdCGmO2TcRBnwXswwbieKMOF5\nskw15Pylpgq/EIkkeZJeei6suMsU+YxkeUcRsrzFcM39pl5eTqnd0QgRnuRJeu4MWLTatKqLuTUB\nl4K3CGXkQ9nG2LbleYfN8sJj/bM/nlkIuRVypinClzxJT4TmDeAJzCRCB7fnDXdB3e+gvWb2xxdf\nB2sfgFxJeiJMkvRSzWXgNUwxsDCl5UD+ErPOUqybTb3D0HMeWo/M8QRtzvRmruiZW2FidLljG1/Y\nRkfhxAnYtcu+GGproavLvuM7hCQ9EbKcUlhzP1TdZPqM7NRzAWqeAU/G9O0r3wfrP+TApNfXBy+8\nAPv22RfD0BBcTqBxpDGSPEnPPwZdtWbwWOFyaWGf6RJwGqjBlIeIgCfLzOwrXG5ZVBEb65+9va98\nC2iLiqJGQ7lNUdVlt09sGQcuBG/2GPdCrwtyKGYd6yim2LJ9jzDCaU5zGecn1eRJeuODcOEVMyNi\nw0ck6c10EPgOcA7z+RMx5U4z9WvLNtkdyaSeC3DyOVjau5xP82mu53rL9t1CC//EP0nSi6uAz6w3\n26nh8puTpwGZBaZXN9XnSXVjzvQiaNJJy5nsGM+27uQgqSmXuejILrE7kkn+MVPGP5tsVrKSTViX\nkfPJp5BCy/YXS8mT9CaM9sL5XVD/mrlfshY2/bkkvShkLYLV95ozlwyHTIETIlLJl/QCPlMAYIJS\nUP8q9NWb+zllZmHWVPn0nsMs6/gmEGEBGHeaOcOb2VMqRCJKvqQ302Ar1L40WVC08kbT9ZgqSe8I\n8E1M8otgmIoQySb5k17AB2N9VGVnc3NZGeVlbkhv4Hx/G2+1d9CXVWGmF6Rl2x1pbIwAbZj16sKU\nlmPemoprIXex1YEJYY/kT3pBq/Lz+at167ixvBTcF3mxsZHzNUfpK7kJCquTN+lFISMfVm43NfPc\n6XZHI4Q1UibpeVwu8tPTKUpzAz625abz6RWVdOR6wHOOY12neL2tlZHCa6BiK3SegbbjoEOYqzXS\n5bziobWYtW13AwOR7UK5zNi89BwL4xLCZimT9GbaVFTEusJCtHKBOsMPO2o5cvQwI6segNINpijo\n4X8hpAmqWpvLaCepAf4PJvl5bY5FCAdJ+qS3JCeHOxcv5q7KSqqyJy9h3S4XkzOVAtxcXMiXNqxl\nr7+LfTVPc5Nu5c7NG1BhzMpvHh5mX0sLFwYiPLWyUgDTWxtBwvNkQeX1sPh6s7yjEMkk6ZPe0pwc\nPrVmDXdVVs77vJtKS7mptJTMmhreOLSbOzZu5H/ccAMuFfqK2G93dNA4NGRv0tMz/h+B9ByovgPW\nftCSiIRwlMRNei6PWeaxfPP07aO90HTQzLmJwK1lZXz1uuu4sbSU0NOdg9QCLwN7iWj2hRDJLoGT\nXpop97Hpz6Zv770Eg22onguku1xkut1hna3dVFbGTWUJPAr3HPAvwPHIXu7ymHqsjqtSIoRFEjfp\nLaAyO5v7li7l7spKVuU5ZM0Mh3NnwNJ3w5KbzaQVIZJRYiY9T6YpJDCzmNoUpVlZPFRdzf3LlsUs\nDH8gwJDPx3jA1DLqHR/HG3BAXaMIudNNB8a6h6zZn28MfKNE1L44PhhZh7hvDMb6zOR6Md3Ee+rF\nSx99dNJp2b676WYs0nmOcZaASU+Ztrxlt5qhJTbqHhvjxYYGDnV0ANAxOsq5/jkWdUhB7TXQ8JpJ\nROEa7YXe+vBf13YcDj9hLtPFdKM9ZjhpPfX8kB/yG35j2b4HGeQoRy3bXywl3p+GUmZu1NoHbAvB\nHwjQ5/VS29fHzvp6XmposC0Wq6TnmhKEnizr9tnTDrWHTen3sHmZNnXOlWZinJhCPZfxgavXXBeG\n3wveIRiihZ3sxONykeXx4Ha5AK503M12Yj7bYyNeL2N+DRRgVrYHWBJhdEOYf3BzpZRNGrnMPw3I\nT4BBxhkLsypu4iU9B+j3evnVpUv8trGRE93dC7/A4ZQbltwC1e+B4jUW7vgWYBUTf8fhqQde4EqH\nTH6VWeHoz6FhAAAY5klEQVQzr8qy6FLOQDNc2js5sKEsJ4dtFRUUZ4c/BdPr93OktZUznWPAg8Cd\nUUa3F/MPPgjANip4LyvmHUHRwyh7uchJOsI60oJJTyn1BPABoE1rvSW4rQh4FqjGFCLfobXuCz72\nOPAo4AM+r7W2ZSWUMb+fpuFhzvaZ04Vsj4fijAyyPNHn+RGfj9fb2vjVpUtR78sJXG6z2M+qeyze\n8TpgO5F9tR4B3uJK0ssuMZ0sTqpEnGg6TkH7icmkV5iZyaayMqoLwy/+Oerz0To4yJlOgHcBD0cZ\n3QjwGyaS3moW8X6uwTVP2rtMP2foDDvpuUJ4zpPAn8zY9hiwW2u9FtgDPA6glNoA7ADWA/cB31Mq\njPEiFmoZHuandXX850OH+M+HDvFkbS2NQ0N2hCKEcJAFv4O11vuVUtUzNj8I3BH8+SlgHyYRPgA8\no7X2AZeUUnXATZjv7OhlLTJf+ZlFcz/HnQ55i+nNXcb+Ke1C9SrA8u5+RnzTuwSLMjKoyMoi3b3w\nwDRfIEDryAhnenvpHkuMnqqFZC2CnHLImuctFSKZRHqtV6a1bgPQWrcqpSZG81ZhavROaApus8bi\n603d8sKZOXiKzEJznbb4ummbzw818v3GNygcOTVt+12VlXzymmsoDSHpjfr9vNTQwL9evMiZvggK\n1DlQ5Y2w+k/MKmdCpAKrOjKimOkZhoKlZqjKfNKyZx1Z29Vxmtc7LsDo9K7EwIBiVUc35RnT34rK\n7GyW5uQw9ercGwhQ093N7ubmyH8HhymsNm1lQqSKSJNem1KqXGvdppSqANqD25uApVOetyS4zX75\nVWYV6PHBaZuP9NTw92f2keGdXiTgY6tW8ak1axJz/q0QYk6hJj0F0z7/O4FHgG9gum1enLL9aaXU\ntzGXtasxK67aLyMfSq9eF6NVB2htOwe6Z9r2igHF8pYWXIBLKVbk5ZGXtsAgsQSSWwF5lVIGXqSe\nUIas/BwzCKdYKdUAfBX4OvC8UupRzIiqHQBa61NKqeeAU5jhpZ/VOpTSwzYqXgNbPgGB6YXnXm1+\nlYvv7AatSXe7+czatdy3dOkcO0k8FdvMdLPcCrsjESK+Qum9/dgcD22f4/lfA74WTVBXyauEgmWQ\nH4Okk1U0a9dl01A7Te0XQWvS3G5Wu5fjoYJ6zlgfgw1yykxVLhXKoCUhkkhizMgo3wobPgy55fE7\nZsU2k2i1xqcUv8vO5iA+WjgRvxiEEJZLjKSXtchchi408dJK2cXmhumabgaavSNQvAWW9kDPRRhs\niV88FslfAkUrzU2IVJQYSc8p3Omw7DZzBnjy2YRMeuVbYOOfmctb6ZoWqUiSXjhcbnP2506HJe8C\nHYCuszDYandkIcvINzk7zcJqKkIkEmnGjkRaNiy/Ezb9OSxabXc0QogwOPtMr3CFKRRattFZ3Ywu\nt6ncnFNqqjgngMLlppJK2SZZ/0KkNmcnvbJNcO3DpiPDSUkvAZVugK1/YdryXMkzxlqIsDk76Xky\nTAGBtPCLHIrp3PJWCgFIm54QIsU480xv0WqouNasayvXYpboOgs1z8a317YtFwIFRPbV2gyctzgg\nIXBq0itZB1sfDg4OlsFkVug4CZ2n43vMaZOuw111BiJbW0OIBTgz6aFMx8VE50XrMWg+ZMbFOclY\n/+SCA3a7ALyCKd7fNmX71q1w111QUBCnoocWa2yEPXuAi3ZHIpKEQ5NekA5AwA9tx+Dok5Gt/pwq\nLgJPcHVh/i1b4AtfgEStEPPGG3DhAgxI0hPWcHbSa6+Byweg9ajzzvKEEAnJWUnP5TFTvDwZZlHv\nzlqoeZY07wjpuIHoR9X60YzjRyuFy5WGlYu1aR0gEPCh45mgfcAoZq3kqWseezyQmQnZ2ea9FEIA\nTkt6i1abCf0V104bULaVCm5jGW4LOjUu0MN+GhjLW0RZ2WYyMvKi3ueEkZFu2tpOMDTUtvCTrdKA\nWS50D9A4ZfumTfDBD8Ltt0NBQfziEcLhnJX0CpbB6vvMehZTrKWYP2U9aRac6e2ngRO0M5hdSlXV\nTeRaWKOvt/cS/f1N8U16rcCvMQvET7V6NXziE7BmTfxiESKO3CjySGcRZhyWjwAjePEu0O3vrKQn\nhBAhyieD7axkPaUAtDDAPi5xkd55XydJL9lkZUFREZSVQRItZCTETDmkcz2VXB+8f4oOTtIhSS/l\nrFsHO3bAbbdBcbHd0QjhOM5IemnZkFkE2aWmB1csbBSz2vBFTM/thMpKuO8+MyhZzCrgg5Geq5ZA\njlpm4axrTAmHcUaGKVwBq+4xy3NlXL02rZhFM/Bz4GXMbAwRMu8wXNpnJvlYaeXd5s9YOJszkl52\nCVTdKKvVhKMPeBP4Y/B+Xh5UVcE115h2vSQ1NgDDHeAbi3wfo73QdBAaXrMuLoBFq6zdn4gNZyQ9\nEb2VK+GRR8y4vIrkXcG7rwHqfgsDzZHvwz8OfY0LP08kJ0l6iWYYqAeOAN1TthcXwy23wPXXz/66\nBDc2AF11ph3u8lsw0GR3RCJRSdJLNB3AL4DfApfsDSWeBprgzK+CnRDdCz9fiLk4I+mNdEPbcRjq\nmL69rx60nxYGOUgTHgsKPZ+li2G8Ue/HNiPAKeBw8H5BgZl9ccMNST3dbHzQFEIVqaGZAQ7SFNbE\n0wb66GN0wec5I+n1XoKTz5tCA1ONdIN/nGO00sIAyoK5twOM0cUwSTOyYNkyePRRUzNvyRK7oxHC\nEkdo5TL9YX3mR/DSPm381uyckfTGB8xtDoVUsor1uKI40+ukk9Ocpof+iPfhSLm5ZkDyunV2RyKE\nZToZppPhmOx7waSnlHoC+ADQprXeEtz2TeCDwBhmJYNPaa37g489DjyKKXr0ea31rmiDvJ3b+Ryf\nI43Ip1Ud4ADf4Tv00BNtOEKIBBbKmd6TwHeAn0zZtgt4TGsdUEp9HXgceFwptQHYAawHlgC7lVLX\naK2jqlReQgkb2Ug66RHvo402spgcvzY62kN7ew39/dGPXfB4ssifURkGMBVQdgHnoj7EpGaml5Dq\n7IS9e6G93cKDOMjZs8n7uwlbLJj0tNb7lVLVM7btnnL3APCnwZ8fAJ7RWvuAS0qpOuAmri5ibruB\ngWZGR/twuaIvV5WTU8bKldvxeDKnP3AW+B6QMdurIuRl+lCVhgZ44onkHZA8Ogrd0l0rrGNFm96j\nmEEUAFWYeQITmoLbolJDDU/xFJ4owj3FKTqY7B32+8fx+8ejDQ2AQMBHW9txPJ4sRkenVHgYwbwD\nsTQ2Bi0tMT6ICEVXHZz9jd1RzG2g+eoBEqkoqqSnlPo7wKu1/sWCT47Ca7zGMY5FtY9xxumPUSfG\n2Fg/TU0HUcqFz7dwl7lITs2H4r/MZjgCPhhfuHMz6UWc9JRSjwDvB+6asrkJmLrs1hLmPde5YcrP\nlcx1UjgEIXRER6oWc/UdPMIW4Gbmf2eGgIPAGXNX6wBe72w9TUuDOyuNIr6R4MFOzfpoeTncfLOZ\ndhuOw4fhrbeA0g1mneFwDHdC+0kY6Zq2OZ+llLKBdHLC298sRumlg1MM0hr1vuLFN2JuiaJ7ZIQj\nra1c6jVXJ2U5OSwvLCQryeswhpr0FFNW3VZK3Qv8LfAerfXUqd87gaeVUt/GZLDVmE/sHKLu2LXA\nM5jsFUx6twJfgXk/ty3A/+ZK0pvbNcB/ALZFEV8n8D+ZK+lNDNN7z3vC2+s//AMcPAi68kbY8vHw\nXtx2woyhnJH0FrGazXyMPBaHt79ZdHOOo/w4oZJeomkfGuLVS5dwBReOum7xYspyciTpKaV+DtwJ\nFCulGoCvYtJCOvBycDWxA1rrz2qtTymlnsN8Qr3AZ+fvuXXCEOEcmDr+LwMT1nxJbwRC60hOA/KJ\n7PccB/YDr2HORmfn8ZgCK0VhHuJKv4cnI/xyXmnZoK7uAHKTRjq5ZBB9ebA0cnBN+fNcxjJu4zYW\nW5BQJ/TSy372UzvP+5vMAloz5p9cQq++r4/9DQ1cU1zMqqIiMjzOGMZrtVB6bz82y+Yn53n+14Cv\nRROUAJP0/g34v5ghj6ltJSv5DJ/hZm62bJ8XuEAffSmb9GZq6u+ndXCQIa+XJfn5qZv0UsZ1mPPZ\n7RDFGGiLeTHjv2NDKYhqAOUM3ZznFM9bcqY3TCd9UwYkunCRQQaZZM7zqvBUUMFH+AjFFLOXvZwl\ntSf3asAXCFDf28srFy6wpriYNcXFpLmjH9blJDYnvZkfuXguSq2n//9GzBDrkhBfHrNQZ8SVQHq4\nQA8XmD32iTcs3MdiZxGL2MEONrCBVlpTPulNaOzvp7G/n3G/nxVFRXhcLlQSLRhvc9L7u+D/NwDv\nA6xbg3ZhNZha63sxZYjDkAf8O2CiUm5bcFeWDFfQwZ29DLxhxQ7nPlLUOeYazL9b1EMxZ9EM7Ga+\n9kyrlFPOJ/gElVTyMi9zztIpNInrYm8vv6urY11JCetLS690eCQ6m5PeP2Au4e4H1gGFmGvL6EtI\nLewM8P/AXWcOGc4MtzzMbOQPBO/XYGrbWZb09gLfsmJnMbYS+BTThx5Z5Qhm1aPYJ71SSvkwH6aa\nas4F/xPQ0NdHQ18fAa1ZUVREhtuN2xWPz2Zs2fwbPIwZn3cK+CfgOYh3QYBtwH8CHmT+HtsklCRf\n3CLGzvf08JuzZznT2Wl3KJaw+UxvB6ZIy0HM0l5DmFoFE9ddmUA2Mc3N64G/ZPqQatsMAwOYMTGx\nF/3lbXLx4KEg+N8QQ/ik1xyYPOPzuFxUFxaSnuAdGzYnvYnBu6sx45qPYy55J3r/7sScgiXpZPqr\n7MW8D/OM5xYxU0UVD/Mwy1jGTnbKZe4MtZ2djPl8bK2oYGW4A0MdxOaktyx4ywHqgKOYS9yJAZOj\nwGZMQ3kB1nSZjgB9kNUFBT5YBDjmi+sd4AdxO5rVQ1YSXRllfIAPUEopl7hEL7300Yc3kZcXsFBj\nfz+X+/vJSktjUVYWo77EPBN2yDi9DcDngN8B/wpXqqEcAP4XpmLVh7Em3FrgBdj0Gvxpl5l2lrxL\nS4gILGc5f8lfsoIVvMALXEqlFZgWoIHTHR30jIzQ2N8PFo6bjBeHJL2JMz4X5ozvJGbOaW3wlo05\n4ysn9IF0c7kM7ITq42bYyZood2eJbqAreIOSktCnlS1bBtnZkR1V2vRmV04593EfCsU+9knSm2Fi\nHJ+RhhmzFe0YxzYmr/BiyyFJb8IW4IvAS5hCABPj5/YDvZiOjx32hBZT+zG/73GUgve9Dx56KLRX\nFhfDihWxjE2I+QwDLwInotzP+eC+Ys9hSa86eBvHnPHVYkqaTJzxFQNrg891ARVEV7bJKWqBX1Je\n7qWyErZvhx1xyO3SpieiN4Zpi37H7kBC5rCkN+F64DHgBeCnTH4D7GWyPF8W8ElMe19yeO974ROf\ngLVrF36uFeTyVqQihya9iTa+XsyqOnWY1XDqgjcwSW8JZnDzUuaewjYQfO1EG0Qt8TqNXlgrJrYG\nQHPNNXD//TaHJESSc2jSm3ALUIYZuPwjppdYGgd+i0kYj2Kmss2mAVMJ60jwfic4pjDlAczvdZp4\nNeJOJZe3IhU5POktDd5aMfMw6+BKT5o/eL8dWI4p1LkSMzH2IpPDXk4Ce5hMek5yGXiVpUv7WblS\nOiSEiAeHJ70Jt2Eud38E/MuMx4aAXwMXgL/CzCv7Bab9D8zlbUN8wozQ7bfDZz4T/6QnbXoiFSVI\n0qsK3i5izo7OYpIcmEveC5jktgqoB15l+kqUTtOI+R1OAj4qK+Fd74L0yNcyF0KEKEGS3oS7MKOJ\nv8tk0pvQC/wSM5DZ6evAvoP5Hc5gptrZQ9r0RCpKsKQ3MS7vbkwJqpNwZVK4F3OWlwi6gBOsWtXG\nxo2webM9ZZ7k8lakogRLemAGJd+Dma/7fyCBK2HcfDP8zd9AdbVZ1UwIEXsJ+FFTmGEs2ThjCclw\nXMRUknkTGKWoCNasgQKbCh7I5a1IRQmY9BLZRL3AU5iOFyFEvEnSi6shoIm1a7u58Ua49VZ7e2yl\nTU+kIkl6Nrj+evjyl01bXkaG3dEIkVoSOOmlAXdgKiG/iVmSzGnqMSWjXg/efwfoJzPT1MzLy7Mv\nMpA2PZGaEjjppWPm214H/HecmfTqgO8xubCRDzNn2Bnk8lakogROegrIwKyvkWZzLHPxM3Vlsw0b\nzJSz7dshJ8WWmxTCKRI46SWeLVvgS1+CVaucseasXN6KVCRJL46UArcbolkkvqMD9uyBU6eii2X/\nfggEotuHEIlowaSnlHoC+ADQprXeMuOxLwF/D5RorbuD2x7HFLjzAZ/XWu+yPOoUpDX4/dDaCs8+\nC7/6ld0RCZGYQjnTexL4DvCTqRuVUkuA9zFlwqtSaj1m5Z71mLLGu5VS12gtTebR6u2FP/wBdu+G\nkyftjkaIxLVg0tNa71dKVc/y0LeBvwV2Ttn2IPCM1toHXFJK1QE3AW9ZEeyMyDAVSvqJqEfUi5kU\n0bfQE0MwwPSizjEwMAC//S387GexPY4QyS6iNj2l1ANAo9b6hJreIl/F9EJ2TcFtMTBRLv53wNvh\nv7wGk7YLLQilG1PxXQjheGEnPaVUFvAVzKVtlP7blJ/vDN4WooFBTJn4P2CqKUfgfPAmhEgpkZzp\nrcIsSnFMmdO8JcBhpdRNmDO7ZVOeu4TJNRtn8d8iOLzGJLudJNJam0IIZwg16angDa11Daaap3lA\nqYvAdVrrHqXUTuBppdQ/YC5rVwMHrQu3B1OA8xXMeriJZWgIGhsjG6PX3AyDg9bHxNgADDSH95qR\nLvCPYwZeN2NKZlmtiakDu4WwSihDVn6Oue4sVko1AF/VWj855SmayYR4Sin1HKZ2khf4rLU9t3uB\n5zElmhLPsWPwrW9Bbm74rx0eNq+3XNNBk8TCMdIDgy2YEv3fAxbFILAezBrFQlgrlN7bjy3w+MoZ\n978GfC3KuGboxCzpuBczgT8x1debm6P0nDe3iPTi/PVIhJguirkBVtgX4vP2A/8V2B27UETS2hfy\n35lIBTZPQ9tHaD22dcCLmCtmkYoGGOA0p/FE8Cf7c35OLuG3KdRRxzDDYb9OOJvMvRUJ4Tzn+QE/\noIDwFxQ5xzkaIljwvZNOmuYbfCASkiQ9kRC66eYAByJ+/XkZlCmClF3TYpVSMh9XCBETWus5B4bZ\nlvSEEMIONvfeCiFEfEnSE0KkFNuSnlLqXqXUGaXUWaXUl22MY4lSao9S6qRS6oRS6nPB7UVKqV1K\nqVql1B+UUuF3G1oTn0spdTg4xc9JcRUopZ5XSp0Ovnc3OyE2pdTjwXiOK6WeVkql2xWXUuoJpVSb\nUur4lG1zxhKMvS74nt5jQ2zfDB77qFLqBaVUfrxjmy2uKY99SSkVUEotmrIt/Li01nG/YZLtOaAa\ns6rPUWCdTbFUANcGf87FzH1aB3wD+E/B7V8Gvm5TfF8AfgbsDN53Slw/Bj4V/NkDFNgdW/Dv6QKQ\nHrz/LPCwXXEBtwHXAsenbJs1FmADcCT4Xi4Pfj5UnGPbDriCP38d+Fq8Y5struD2JcDvMRO9FwW3\nrY8krrj9Qc74BW4Bfjfl/mPAl+2IZZbYfh38xz8DlAe3VQBnbIhlCfAyZgT3RNJzQlz5wPlZttsa\nG1AUjKEo+EHYafe/ZTART00ss8Yy8zOAKRR5czxjm/HYQ8BP7Yhttrgwk+43z0h6EcVl1+VtFdA4\n5f5lYlZsNHRKqeWYb5kDmD/MNgCtdStQZkNIE9Wpp3axOyGuFUCnUurJ4KX3D5RS2XbHprXuAb4F\nNGDKtPRprXfbHdcMZXPEMvMzEcMCvCF5FPi34M+2xja1aPGMhyKKSzoygpRSucAvMYsZDXL16ohx\nHdujlLofsxjTUYJVbOZgx5gjD2aV9e9qra8DhjDfuna/ZysxzQHVQCWQo5T6uN1xLcBJsQCglPo7\nwKu1/oUDYpkoWvxVq/ZpV9ILs9hobCmlPJiE91Ot9YvBzW1KqfLg4xWYUs3xdCvwgFLqAvAL4C6l\n1E+BVpvjAnNm3qi1PhS8/wImCdr9nt0AvK617tZa+4FfAe92QFxTzRVLE7B0yvNs+UwopR4B3g9M\nra5kZ2xTixZfZLJocRkR5hG7kt7bwGqlVLVSKh34KNMXGIq3HwGntNb/OGXbTuCR4M8PYyoexI3W\n+ita62XalO76KLBHa/1J4CU74wrG1gY0KqXWBDfdDZzE5vcM0wl1i1IqM1jV+25MbUc747pSgDdo\nrlh2Ah8N9javwPICvAvHppS6F9Oc8oDWemxGzPGMbVrRYq11hdZ6pdZ6BeYLd5vWuj0Y15+FHVcs\nG0oXaKy8F/NHWgc8ZmMctwJ+TA/yEeBwMLZFmFpWtcAuoNDGGO9gsiPDEXEBWzFfXkeBf8X03toe\nG+ZDexJTafYpzOgAW+ICfo4pLT2GaWf8FKaTZdZYgMcxPZCngXtsiK0Os6Tr4eDte/GObba4Zjx+\ngWBHRqRxyTQ0IURKkY4MIURKkaQnhEgpkvSEEClFkp4QIqVI0hNCpBRJekKIlCJJTwiRUiTpCSFS\nyv8HsMuPA/SmB5sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2288430dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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A3/42DA1tw/1paOwSZRW9facM2Hw29Gk5jIwzQ6ImQbOq5/CkDdeoC6vPSsqRY7w5Qn6y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\nOY6TK7zoOY6TK7zoOY6TK7zoOY6TK/4CWnWe2YDHDe8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f226439f6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ground_truth = spectral.imshow(classes = output_image,figsize =(5,5))\n",
    "predict_image = spectral.imshow(classes = predicted_image.astype(int),figsize =(5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# f_out = open('Predictions.pkl','ab')\n",
    "# pkl.dump({'11x11_aug':predicted_results}, f_out)\n",
    "# f_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
